This talk addresses exploratory search in large collections of historical texts. By way of example, we apply our method to a collection of documents comprising dossiers of the former East-German Ministry for State Security, and classical texts. The bases of our approach are topic models, a class of algorithms that define and infer themes pervading the corpus as probability distributions over the vocabulary. Our topic-centered visual metaphor supports to explore the corpus following an intuitive methodology: First, determine a topic of interest, second, suggest documents that contain the topic with "sufficient" probability, and third, browse iteratively through related topics and documents. Our main focus lies on providing a suitable bird's eye view onto the data to facilitate an in-depth analysis in terms of the topics contained.